Personal identification and security checks represent an integral part of maintaining security in a wide variety of environments, ranging from, e.g., transportation terminals to computer security. A variety of systems and methods exist for identifying an individual based on personal biometric information, which is compared to previously stored data, or using identifications that include photographic images, which may be scanned at an entry point. Unlike biometric techniques, such as fingerprint recognition and the like, face recognition bases identification on unique bio-information but does not require bodily contact with a recognition apparatus or even cooperation by the individual seeking entry.
Conventionally, facial-recognition approaches to identifying and/or verifying a human face first detect the presence of the face (and/or facial features) in a digital image or a video frame and then compare the detected face and/or facial features with those stored in a facial database. A highly accurate detection rate of the face and/or facial features is thus critical in the facial-recognition system.
A face in an image is typically detected by first transforming pixels in a scanning window of the acquired image into various regions or discrete features; a classifier trained on example faces then determines whether pixels in this scanning window are part of the face. This scanning window slides through the entire captured image to determine the location and size of the face. Accurate face detection may be challenging because the appearance of the face can depend upon changes in illumination, posture, and movement. For example, if the person moves during face detection, a shaded region may appear on his/her face due to changes in the spatial relationship between a light source and the person; this may reduce contrast between the face and background of the captured image, thereby increasing the difficulty in distinguishing the face from the background. Reliably detecting a face in a cluttered visual environment represents another challenge. Moreover, these difficulties may be exacerbated in poor lighting conditions, where facial detection may fail altogether, and have limited the widespread adoption of facial identification.
Consequently, there is a need for a facial detection technique that can identify a face and/or facial features reliably and that is robust against variations in ambient illumination and face movement.